import xml.etree.ElementTree as ET
import numpy as np

Sleep Accuracy

import pandas as pd

results_list = []
XTIMER_BACKOFFS=[40, 30, 22]
for i, backoff in enumerate(XTIMER_BACKOFFS, start=1):
    file = f"data/sleep_accuracy/xunit_xtimer_usleep_set_XTIMER_BACKOFF_{backoff}_now.xml"
    root = ET.parse(file).getroot()

    for d in root.find("testcase[@name='Measure Xtimer Set Accuracy Microseconds Template']").findall(".//property"):
        duration = d.get("name").split('-')[-1]
        results = [float(v["diff"]) - (int(duration) / 1000_000) for v in eval(d.get("value"))]    # diff from sleep time
        results_list.append({
            'type': 'xtimer_set',
            'backoff': backoff,
            'duration': duration,
            'results': results,
            'mean': np.mean(results),
            'max': np.amax(results),
            'min': np.amin(results)
        })
    

    for d in root.find("testcase[@name='Measure Xtimer Usleep Accuracy Microseconds Template']").findall(".//property"):
        duration = d.get("name").split('-')[-1]
        results = [float(v["diff"]) - (int(duration) / 1000_000) for v in eval(d.get("value"))]    # diff from sleep time
        results_list.append({
            'type': 'xtimer_usleep',
            'backoff': backoff,
            'duration': duration,
            'results': results,
            'mean': np.mean(results),
            'max': np.amax(results),
            'min': np.amin(results)
        })

sleep_accuracy = pd.DataFrame.from_dict(results_list)

import plotly.graph_objects as go

xtimer_set_traces = []
xtimer_usleep_traces = []
for backoff in sleep_accuracy['backoff'].unique():
    xtimer_set = sleep_accuracy.loc[sleep_accuracy['type'] == 'xtimer_set']
    data = xtimer_set[xtimer_set['backoff'] == backoff]
    xtimer_set_traces.append(go.Scatter(x=data['duration'], y=data['mean'], name=f'xtimer_set / {backoff}'))

    xtimer_set = sleep_accuracy.loc[sleep_accuracy['type'] == 'xtimer_usleep']
    data = xtimer_set[xtimer_set['backoff'] == backoff]
    xtimer_usleep_traces.append(go.Scatter(x=data['duration'], y=data['mean'], name=f'xtimer_usleep / {backoff}'))


sleep_accuracy_fig = go.Figure()
sleep_accuracy_fig.add_traces(xtimer_set_traces)
sleep_accuracy_fig.add_traces(xtimer_usleep_traces)
sleep_accuracy_fig.update_layout(
    title="Sleep Accuracy with multiple XTIMER_BACKOFF",
    xaxis_title="Specified Sleep Duration [us]",
    yaxis_title="Real Sleep Duration [s]",
)

# add range slider
sleep_accuracy_fig.update_layout(
    xaxis=dict(
        rangeslider=dict(
            visible=True,
        )
    )
)

# fig.write_html("sleep_accuracy.html", include_plotlyjs='cdn')
sleep_accuracy_fig.show()

Sleep Difference (Jitter)

file = "data/sleep_jitter/xunit copy.xml"
root = ET.parse(file).getroot()

results_list = []
for d in root.find("testcase[@name='Measure Sleep Jitter 50']").findall(".//property"):
    if d.get('name') == 'trace':
        # print(d.get('value'))
        duration = [t['diff'] for t in eval(d.get('value'))]
    if d.get('name') == 'focus':
        focus = eval(d.get('value'))[0]
    if d.get('name') == 'intervals':
        intervals = eval(d.get('value'))

# print(focus)
intervals = list(map(lambda i: int(i) / int(focus), intervals))
# print(intervals)
# print(duration)
# print(len(duration))

timer_count = [1 for i in range(len(duration))]
for i, count in enumerate(timer_count):
    x = i + 1
    for itv in intervals:
        timer_count[i] += int(not x % itv)
    # timer_count[i] += int(not x % 2) + int(not x % 4) + int(not x % 8) + int(not x % 16)
# print(timer_count)
# print(len(timer_count))

xaxis = range(1, 51)
# print(xaxis)
# print(len(xaxis))

import pandas as pd

sleep_jitter = pd.DataFrame({'second': xaxis, 'duration': duration, 'timer_count': timer_count})
# print(sleep_jitter)

import plotly.express as px

sleep_jitter_fig = px.scatter(sleep_jitter, x='second', y='duration', size='timer_count')
sleep_jitter_fig.show()